KASSPER analysis of 2.D parametric STAP performance: Further results on time-varying autoregressive “Relaxations”

We continue our investigation into the new class of two-dimensional autoregressive relaxed models (ldquorelaxationsrdquo) for space-time adaptive processing (STAP) applications. Previously reported results on the DARPA KASSPER simulated dataset for airborne side-looking radar are now complemented by STAP performance analysis for all range bins and varying antenna-array errors. We discuss the variability of signal-to-interference-plus-noise ratio (SINR) performance associated with the changing terrain conditions across all 1000 KASSPER range bins, and more closely investigate the impact of antenna errors and training data inhomogeneity. Performance improvements due to the previously proposed regularisation of the parametric models are also demonstrated in more detail.